Active learning (machine learning) ≈ Active learning (machine learning)
View article: A survey of transfer learning
A survey of transfer learning Open
Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the…
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BELAJAR DAN PEMBELAJARAN Open
This study aims to discuss the learning and intruction which is an activity conducted by teachers and students. Learning is the process of changing a behavior and knowledge. Learning process becomes one system in intruction. The intruction…
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The Flipped Classroom: A Survey of the Research Open
The Flipped Classroom: A Survey of the ResearchRecent advances in technology and in ideology have unlocked entirely new directions foreducation research. Mounting pressure from increasing tuition costs and free, online courseofferings is o…
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Machine learning for combinatorial optimization: A methodological tour d'horizon Open
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-…
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Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom Open
Significance Despite active learning being recognized as a superior method of instruction in the classroom, a major recent survey found that most college STEM instructors still choose traditional teaching methods. This article addresses th…
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Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math Open
We tested the hypothesis that underrepresented students in active-learning classrooms experience narrower achievement gaps than underrepresented students in traditional lecturing classrooms, averaged across all science, technology, enginee…
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A survey of machine learning for big data processing Open
There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel lear…
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Meta-SGD: Learning to Learn Quickly for Few-Shot Learning Open
Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with…
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Interactive machine learning for health informatics: when do we need the human-in-the-loop? Open
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions.…
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Less is more: Sampling chemical space with active learning Open
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor resea…
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What Do We Mean by Blended Learning? Open
The term blended learning is used frequently, but there is ambiguity about what is meant. What do we mean by blended learning? What, how and why are we blending? In this paper different definitions, models and conceptualizations of blended…
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Contrastive Representation Learning: A Framework and Review Open
Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development…
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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems Open
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of info…
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The Challenges of Online Learning: Supporting and Engaging the Isolated Learner Open
Higher education providers are becoming increasingly aware of the diversity of their current and potential learners and are moving to provide a range of options for their engagement. The increasingly flexible delivery modes available for u…
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Cost-Effective Active Learning for Deep Image Classification Open
Recent successes in learning-based image classification, however, heavily\nrely on the large number of annotated training samples, which may require\nconsiderable human efforts. In this paper, we propose a novel active learning\nframework,…
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Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis Open
Background Blended learning, which combines face-to-face learning and e-learning, has grown rapidly to be commonly used in education. Nevertheless, the effectiveness of this learning approach has not been completely quantitatively synthesi…
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Fast kernel classifiers with online and active learning Open
Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should at least take a brief look at each examp…
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Transfer Learning in Deep Reinforcement Learning: A Survey Open
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the prom…
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Deep Bayesian Active Learning with Image Data Open
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) metho…
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Students’ perception of online learning during the COVID-19 pandemic Open
The COVID-19 pandemic has disrupted teaching in a variety of institutions, especially in medical schools. Electronic learning (e-learning) became the core method of teaching the curriculum during the pandemic. After 8 weeks of only online …
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The Effectiveness of the Project-Based Learning (PBL) Approach as a Way to Engage Students in Learning Open
The prevalence of project-based learning (PBL) has increased significantly, contributing to serious discussions about its advent. PBL’s critics doubt whether accentuating the practice supports teachers in using a technocratic method in edu…
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Small data machine learning in materials science Open
This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of deali…
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Federated Learning: Collaborative Machine Learning withoutCentralized Training Data Open
Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm without transferring data samples across numerous decentralized edge devices or servers. This strategy differs from standard…
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Machine learning algorithms review Open
Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machi…
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A systematic literature review of personalized learning terms Open
Learning is a natural human activity that is shaped by personal experiences, cognitive awareness, personal bias, opinions, cultural background, and environment. Learning has been defined as a stable and persistent change in what a person k…
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Continual Learning for Robotics: Definition, Framework, Learning\n Strategies, Opportunities and Challenges Open
Continual learning (CL) is a particular machine learning paradigm where the\ndata distribution and learning objective changes through time, or where all the\ntraining data and objective criteria are never available at once. The evolution\n…
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Adaptive e-learning environment based on learning styles and its impact on development students' engagement Open
Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose…
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Deep Bayesian Active Learning with Image Data Open
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) metho…
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Quantum-Enhanced Machine Learning Open
The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work …
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Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally Open
Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging. Annotating biomedical images is not on…